2020-01-09

Title

What lies behind the causal impact of body mass index (BMI) level and change on human health? Added value from complementary study design and deep metabolomic phenotyping

 

Start date: 01/10/2017

 

End date: 01/04/2021

Aim

Identify metabolites that sit on the causal pathway from increased adiposity to disease

 

Objectives

  1. Identify diseases causally associated with increased adiposity
  2. Identify and describe appropriate instrumentation of increased adiposity
  3. Identify metabolites causally associated with increased adiposity
  4. Design and implement methods and rules to cluster metabolites
  5. Identify diseases causally associated with metabolites

Overview

Proportion of overweight individuals

Proportion of overweight individuals

Proportion of obese individuals

Proportion of obese individuals

Can we build a flowchart of decisions to achieve this?

Chapter 1

Intro/overview

  • What is the problem (100%)
  • What is adiposity (100%)
    • inc. adipose cells - idea of genetics and metabolites introduced
    • inc. different measures
  • Broad overview of diseases (90%)
  • Application of metabolites and MR (0%)

MELODI analysis: 975,402 articles; 10,828 enriched terms, filtering left 77 terms, 8 categories plus ‘Other’

Intermediates identified between BMI and all targets using MELODI
Cancer Cardiovascular Immune Kidney Liver Neuro_behav Pregnancy Respiratory Other
Primary carcinoma of the liver cells Heart failure Pancreatitis End stage renal failure Liver diseases Depressive disorder Pre-Eclampsia Tuberculosis Metabolic syndrome
Malignant neoplasm of stomach Anemia Inflammatory disorder Kidney Failure, Chronic Non-alcoholic fatty liver Dementia Pregnancy Sleep Apnea, Obstructive Cessation of life
Malignant neoplasm of prostate Dyslipidemias Immunocompromised Host Glomerular Filtration Rate Liver and Intrahepatic Biliary Tract Carcinoma Hypertension induced by pregnancy Pneumonia Malnutrition
Malignant neoplasm of lung Cerebrovascular accident Bacteremia Kidney Diseases Chronic liver disease Chronic Obstructive Airway Disease Diabetic
Common Neoplasm Cardiovascular Diseases Septicemia Kidney Failure Respiration Disorders Multiple Organ Failure
Liver neoplasms Atherosclerosis Lupus Erythematosus, Systemic Renal function Respiratory Distress Syndrome, Adult Fibrosis
Malignant disease Myocardial Infarction Sepsis Syndrome Respiratory Tract Infections Deglutition Disorders
Carcinoma of the Large Intestine Ischemic stroke Respiratory Failure Vitamin D Deficiency
Pancreatic carcinoma Acute coronary syndrome Acute respiratory failure
Atrial Fibrillation
Coronary heart disease
Systemic arterial pressure
Thrombosis
Cerebrovascular Disorders
Acute myocardial infarction
Sinus rhythm
Cardiomyopathies
Myocardial Ischemia
Peripheral Vascular Diseases
Vascular calcification
Heart Arrest
Myocardial rupture
Shock, Cardiogenic
Hemorrhage
Ischemia
Congestive heart failure
Ventricular Dysfunction, Left
Mitral Valve Insufficiency
Hyperglycaemia

Chapter 2

Systematic review

All MR studies using a measure of increased adiposity as the exposure; ~150 articles included

  • Literature search (100%)
  • Screening (100%)
  • Data extraction (30%)
  • Writing (40%)

Chapter 3

Instrumentation

  • Current instrumentation practices (will get from SR)
  • GWAS descriptions (80%)
  • Selecting instruments (0%)
  • Writing (30%)

Chapter 4

Observational analysis

  • BMI/WHR/BF% -> metabolites in ALSPAC/FGFP/Biobank (20%)
  • Writing (0%)

Waiting on Biobank data…

Chapter 5

MR step 1

  • BMI/WHR/BF% -> metabolites analysis (100%)
  • Wiriting/paper (60%)

Chapter 6

MR Viz

  • web app (90%)
  • R package (90%)
  • Writing/paper (60%)
  • App / R package

library(EpiCircos)
circos_plot(track_number = 3,
            track1_data = EpiCircos::EpiCircos_data,
            track2_data = EpiCircos::EpiCircos_data,
            track3_data = EpiCircos::EpiCircos_data,
            track1_type = "points", track2_type = "lines", track3_type = "bar",
            label_column = 1, section_column = 2,
            estimate_column = 4, pvalue_column = 5,
            pvalue_adjustment = 1,
            lower_ci = 7, upper_ci = 8,
            lines_column = 10, lines_type = "o",
            bar_column = 9,
            legend = TRUE,
            track1_label = "Track 1",
            track2_label = "Track 2",
            track3_label = "Track 3",
            pvalue_label = "<= 0.05",
            circle_size = 25)

Chapter 7

Clustering metabolites

  • not started

  • Writing (0%)

  • Priors

    • class
    • subclass
    • biological pathway
    • size
    • shared genetic variants

  • No priors
    • PCA
    • factor analysis
    • Hierarchical clustering
    • density clustering
    • self organising map
    • LDSR
    • ontology
      • have discussed with Ben Elsworth (pipeline can be adpated)

Chapter 8

Rules for instrumenting clusters

  • have ideas
  • not started - in parallel with Chapter 7
  • Writing (0%)

Chapter 9

MR step 2

  • metabolites -> diseases analysis (20%) - will be scaled quickly
  • Final figure for global overview (1%)
    • combining MR step 1 and MR step 2 results together
    • possible to incorporate multiple clustering methods
  • Writing (0%)

Chapter 10

Discussion/limitations/conclusion

  • Final flowchart figure of pipeline
  • not started
  • Writing (0%)